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How to kill inventors: testing the Massacrator© algorithm for inventor disambiguation

Author

Listed:
  • Michele Pezzoni

    (CRIOS - Center for Research on Innovation, Organization and Strategy (University of Bocconi))

  • Francesco Lissoni

    (GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique)

  • Gianluca Tarasconi

Abstract

Inventor disambiguation is an increasingly important issue for users of patent data. We propose and test a number of refinements to the original Massacrator algorithm, originally proposed by Lissoni et al. (The keins database on academic inventors: methodology and contents, 2006) and now applied to APE-INV, a free access database funded by the European Science Foundation. Following Raffo and Lhuillery (Res Policy 38:1617–1627, 2009) we describe disambiguation as a three step process: cleaning&parsing, matching, and filtering. By means of sensitivity analysis, based on MonteCarlo simulations, we show how various filtering criteria can be manipulated in order to obtain optimal combinations of precision and recall (type I and type II errors). We also show how these different combinations generate different results for applications to studies on inventors' productivity, mobility, and networking; and discuss quality issues related to linguistic issues. The filtering criteria based upon information on inventors' addresses are sensitive to data quality, while those based upon information on co-inventorship networks are always effective. Details on data access and data quality improvement via feedback collection are also discussed.

Suggested Citation

  • Michele Pezzoni & Francesco Lissoni & Gianluca Tarasconi, 2014. "How to kill inventors: testing the Massacrator© algorithm for inventor disambiguation," Post-Print halshs-01074536, HAL.
    • Handle: RePEc:hal:journl:halshs-01074536
    DOI: 10.1007/s11192-014-1375-7
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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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